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<a href="_multi_task_kernel_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Special kernel classes for multi-task and transfer learning.</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      T. Glasmachers, O.Krause</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2012</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * </span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * </span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * </span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * </span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_MODELS_KERNELS_MULTITASKKERNEL_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_MODELS_KERNELS_MULTITASKKERNEL_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#include &lt;<a class="code" href="_discrete_kernel_8h.html">shark/Models/Kernels/DiscreteKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_product_kernel_8h.html">shark/Models/Kernels/ProductKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_dataset_8h.html">shark/Data/Dataset.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &quot;Impl/MklKernelBase.h&quot;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment"></span> </div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">///</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// \brief Aggregation of input data and task index.</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">///</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// Generic data structure for augmenting arbitrary data</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">/// with an integer. This integer is typically used as a</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// task identifier in multi-task and transfer learning.</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// \ingroup kernels</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputTypeT&gt;</div>
<div class="foldopen" id="foldopen00054" data-start="{" data-end="};">
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html">   54</a></span><span class="keyword">struct </span><a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">MultiTaskSample</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_serializable.html" title="Abstracts serializing functionality.">ISerializable</a></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>{</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html#a900ec372a87978f0487ecb0067ac0fe3">   56</a></span>    <span class="keyword">typedef</span> InputTypeT <a class="code hl_typedef" href="structshark_1_1_multi_task_sample.html#a900ec372a87978f0487ecb0067ac0fe3">InputType</a>;<span class="comment"></span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">    /// \brief Default constructor.</span></div>
<div class="foldopen" id="foldopen00058" data-start="{" data-end="}">
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html#afecb3cf980d0792da79889176944ca6b">   58</a></span><span class="comment"></span>    <a class="code hl_function" href="structshark_1_1_multi_task_sample.html#afecb3cf980d0792da79889176944ca6b" title="Default constructor.">MultiTaskSample</a>()</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    { }</div>
</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment"></span> </div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">    /// \brief Construction from an input and a task index</span></div>
<div class="foldopen" id="foldopen00062" data-start="{" data-end="}">
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html#ae89401af1b81e452db5cb1dd13e2b5f0">   62</a></span><span class="comment"></span>    <a class="code hl_function" href="structshark_1_1_multi_task_sample.html#ae89401af1b81e452db5cb1dd13e2b5f0" title="Construction from an input and a task index.">MultiTaskSample</a>(<a class="code hl_typedef" href="structshark_1_1_multi_task_sample.html#a900ec372a87978f0487ecb0067ac0fe3">InputType</a> <span class="keyword">const</span>&amp; i, std::size_t t)</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    : <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f" title="input data">input</a>(i), <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a21d7d05fb180df274599ea6a9cd510a7" title="task index">task</a>(t)</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    { }</div>
</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span> </div>
<div class="foldopen" id="foldopen00066" data-start="{" data-end="}">
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html#a5de304c0f2e7d2cfecd56dc305264559">   66</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="structshark_1_1_multi_task_sample.html#a5de304c0f2e7d2cfecd56dc305264559" title="Read the component from the supplied archive.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a>&amp; ar){</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>        ar &gt;&gt; <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f" title="input data">input</a>;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        ar &gt;&gt; <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a21d7d05fb180df274599ea6a9cd510a7" title="task index">task</a>;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>    }</div>
</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span> </div>
<div class="foldopen" id="foldopen00071" data-start="{" data-end="}">
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html#a2d8507c0c1f15728fdfafbc5942fdfaf">   71</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="structshark_1_1_multi_task_sample.html#a2d8507c0c1f15728fdfafbc5942fdfaf" title="Write the component to the supplied archive.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a>&amp; ar)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>        ar &lt;&lt; <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f" title="input data">input</a>;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        ar &lt;&lt; <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a21d7d05fb180df274599ea6a9cd510a7" title="task index">task</a>;</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    }</div>
</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span> </div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f">   76</a></span>    <a class="code hl_typedef" href="structshark_1_1_multi_task_sample.html#a900ec372a87978f0487ecb0067ac0fe3">InputType</a> <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a6ddde745da223186836b1286290e750f" title="input data">input</a>;                <span class="comment">///&lt; input data</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"><a class="line" href="structshark_1_1_multi_task_sample.html#a21d7d05fb180df274599ea6a9cd510a7">   77</a></span>    std::size_t <a class="code hl_variable" href="structshark_1_1_multi_task_sample.html#a21d7d05fb180df274599ea6a9cd510a7" title="task index">task</a>;               <span class="comment">///&lt; task index</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span> </div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>};</div>
</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>}</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span> </div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="preprocessor">#ifndef DOXYGEN_SHOULD_SKIP_THIS</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span> </div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>    BOOST_FUSION_ADAPT_TPL_STRUCT(</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        (<a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">InputType</a>),</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        (<a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">shark::MultiTaskSample</a>) (<a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">InputType</a>),</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        (<a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">InputType</a>, input)(std::size_t, task)</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>    )</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span> </div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> InputType&gt;</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="keyword">struct </span>Batch&lt; MultiTaskSample&lt;<a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">InputType</a>&gt; &gt;{</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <a class="code hl_define" href="_batch_interface_adapt_struct_8h.html#aba1e9bf6ae9ccc91fada2d28466e338a" title="This macro can be used to specialize a structure type easily to a batch type.">SHARK_CREATE_BATCH_INTERFACE</a>(</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        MultiTaskSample&lt;InputType&gt;,</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        (<a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">InputType</a>, input)(std::size_t, task)</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    )</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>};</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>}</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span> </div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="preprocessor">#endif </span><span class="comment">/* DOXYGEN_SHOULD_SKIP_THIS */</span><span class="preprocessor"></span></div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment"></span> </div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment">///</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">/// \brief Special &quot;Gaussian-like&quot; kernel function on tasks.</span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">///</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">/// See&lt;br/&gt;</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment">/// Learning Marginal Predictors: Transfer to an Unlabeled Task.</span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment">/// G. Blanchard, G. Lee, C. Scott.</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">///</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">/// This class computes a Gaussian kernel based on the distance</span></div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span><span class="comment">/// of empirical distributions in feature space induced by yet</span></div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">/// another kernel. This is useful for multi-task and transfer</span></div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span><span class="comment">/// learning. It reduces the definition of a kernel on tasks to</span></div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span><span class="comment">/// that of a kernel on inputs, plus a single bandwidth parameter</span></div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span><span class="comment">/// for the Gaussian kernel of distributions.</span></div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span><span class="comment">///</span></div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment">/// Given unlabaled data \f$ x_i, t_i \f$ where the x-component</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment">/// is an input and the t-component is a task index, the kernel</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment">/// on tasks t and t&#39; is defined as</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">///     k(t, t&#39;) = \exp \left( -\gamma \cdot \left\| \frac{1}{\ell_{t}\ell{t&#39;}} \sum_{i | t_i = t}\sum_{j | t_j = t&#39;} k&#39;(x_i, x_j) \right\|^2 \right)</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment">/// where k&#39; is an arbitrary kernel on inputs.</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment">/// \ingroup kernels</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputTypeT &gt;</div>
<div class="foldopen" id="foldopen00129" data-start="{" data-end="};">
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html">  129</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_gaussian_task_kernel.html" title="Special &quot;Gaussian-like&quot; kernel function on tasks.">GaussianTaskKernel</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_discrete_kernel.html" title="Kernel on a finite, discrete space.">DiscreteKernel</a></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>{</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_discrete_kernel.html" title="Kernel on a finite, discrete space.">DiscreteKernel</a> <a class="code hl_class" href="classshark_1_1_discrete_kernel.html" title="Kernel on a finite, discrete space.">base_type</a>;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a39fef0a907da511f9b3e3c362a6417df">  134</a></span>    <span class="keyword">typedef</span> InputTypeT <a class="code hl_typedef" href="classshark_1_1_gaussian_task_kernel.html#a39fef0a907da511f9b3e3c362a6417df">InputType</a>;</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a74d917dde073cfd8b0e089073191458d">  135</a></span>    <span class="keyword">typedef</span> <a class="code hl_struct" href="structshark_1_1_multi_task_sample.html" title="Aggregation of input data and task index.">MultiTaskSample&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_gaussian_task_kernel.html#a74d917dde073cfd8b0e089073191458d">MultiTaskSampleType</a>;</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a5efa4d99f65019b7b91fe009d032b8ef">  136</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_gaussian_task_kernel.html#a5efa4d99f65019b7b91fe009d032b8ef">KernelType</a>;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span><span class="comment"></span> </div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span><span class="comment">    /// \brief Construction of a Gaussian kernel on tasks.</span></div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span><span class="comment">    /// \param  data         unlabeled data from multiple tasks</span></div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span><span class="comment">    /// \param  tasks        number of tasks in the problem</span></div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span><span class="comment">    /// \param  inputkernel  kernel on inputs based on which task similarity is defined</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="comment">    /// \param  gamma        Gaussian bandwidth parameter (also refer to the member functions setGamma and setSigma).</span></div>
<div class="foldopen" id="foldopen00144" data-start="{" data-end="}">
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#ad3cb825170b89942732ef99333e90ed3">  144</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#ad3cb825170b89942732ef99333e90ed3" title="Construction of a Gaussian kernel on tasks.">GaussianTaskKernel</a>(</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;MultiTaskSampleType&gt;</a> <span class="keyword">const</span>&amp; data,</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            std::size_t tasks,</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>            <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>&amp; inputkernel,</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>            <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a72c07dd2a07d2b10033b758f43ba0fb2" title="Kernel bandwidth parameter.">gamma</a>)</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    : <a class="code hl_class" href="classshark_1_1_discrete_kernel.html" title="Kernel on a finite, discrete space.">DiscreteKernel</a>(RealMatrix(tasks, tasks,0.0))</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>    , <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>(data)</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>    , <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>(&amp;inputkernel)</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>    , <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a>(<a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a72c07dd2a07d2b10033b758f43ba0fb2" title="Kernel bandwidth parameter.">gamma</a>){</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#aefbd61ad0c2f8c4adb06669bd49a41f1" title="Compute the Gram matrix of the task kernel.">computeMatrix</a>();</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span><span class="comment"></span> </div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00157" data-start="{" data-end="}">
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#afcf414a6503122bed82e17c8f242a5eb">  157</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#afcf414a6503122bed82e17c8f242a5eb" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;GaussianTaskKernel&quot;</span>; }</div>
</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span> </div>
<div class="foldopen" id="foldopen00160" data-start="{" data-end="}">
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#aa2b81431e43f111b7ff8d3b6ea9eda58">  160</a></span>    RealVector <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#aa2b81431e43f111b7ff8d3b6ea9eda58" title="Return the parameter vector.">parameterVector</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#afaa2ba692ab64a0edbff60d7ee6794db" title="Return the parameter vector.">parameterVector</a>() | <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a>;</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>    }</div>
</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span> </div>
<div class="foldopen" id="foldopen00164" data-start="{" data-end="}">
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a2529001b1f43ca4cd17625dc793e6f6a">  164</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a2529001b1f43ca4cd17625dc793e6f6a" title="Set the parameter vector.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        std::size_t kParams = <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>();</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#ad5e35d1a10ff36fa72ea787baa40e9ad" title="Set the parameter vector.">setParameterVector</a>(subrange(newParameters,0,kParams));</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a> = newParameters.back();</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#aefbd61ad0c2f8c4adb06669bd49a41f1" title="Compute the Gram matrix of the task kernel.">computeMatrix</a>();</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>    }</div>
</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span> </div>
<div class="foldopen" id="foldopen00171" data-start="{" data-end="}">
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a9d09922eb837683653e3d8db6bffd797">  171</a></span>    std::size_t <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a9d09922eb837683653e3d8db6bffd797" title="Return the number of parameters.">numberOfParameters</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_i_parameterizable.html#aed1e8d1d4dbde387e2f6a25141ed3a20" title="Return the number of parameters.">numberOfParameters</a>() + 1;</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>    }</div>
</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span> </div>
<div class="foldopen" id="foldopen00175" data-start="{" data-end="}">
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#acd73ed4a70e91cc3d8f85c70fac37247">  175</a></span>    std::size_t <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#acd73ed4a70e91cc3d8f85c70fac37247">numberOfTasks</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <a class="code hl_function" href="classshark_1_1_discrete_kernel.html#ab36fd3d4ccf16666cb6fe37f4b5e5246" title="Cardinality of the discrete space.">size</a>(); }</div>
</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment"></span> </div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment">    /// \brief Kernel bandwidth parameter.</span></div>
<div class="foldopen" id="foldopen00179" data-start="{" data-end="}">
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a72c07dd2a07d2b10033b758f43ba0fb2">  179</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a72c07dd2a07d2b10033b758f43ba0fb2" title="Kernel bandwidth parameter.">gamma</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a>; }</div>
</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span><span class="comment"></span> </div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span><span class="comment">    /// \brief Kernel width parameter, equivalent to the bandwidth parameter.</span></div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span><span class="comment">    /// The bandwidth gamma and the width sigma are connected: \f$ gamma = 1 / (2 \cdot sigma^2) \f$.</span></div>
<div class="foldopen" id="foldopen00185" data-start="{" data-end="}">
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#acddb38cbf7f7eb1ddb915020cacd9416">  185</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#acddb38cbf7f7eb1ddb915020cacd9416" title="Kernel width parameter, equivalent to the bandwidth parameter.">sigma</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> (1.0 / std::sqrt(2 * <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a>)); }</div>
</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span> </div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    <span class="comment">// \brief Set the kernel bandwidth parameter.</span></div>
<div class="foldopen" id="foldopen00189" data-start="{" data-end="}">
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a4fa4e4f015f87d9ec01865c4f9ecbe39">  189</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a4fa4e4f015f87d9ec01865c4f9ecbe39">setGamma</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a72c07dd2a07d2b10033b758f43ba0fb2" title="Kernel bandwidth parameter.">gamma</a>)</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>    {</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(<a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a72c07dd2a07d2b10033b758f43ba0fb2" title="Kernel bandwidth parameter.">gamma</a> &gt; 0.0);</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a> = <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a72c07dd2a07d2b10033b758f43ba0fb2" title="Kernel bandwidth parameter.">gamma</a>;</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>    }</div>
</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span><span class="comment"></span> </div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span><span class="comment">    /// \brief Set the kernel width (equivalent to setting the bandwidth).</span></div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span><span class="comment">    /// The bandwidth gamma and the width sigma are connected: \f$ gamma = 1 / (2 \cdot sigma^2) \f$.</span></div>
<div class="foldopen" id="foldopen00198" data-start="{" data-end="}">
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a0203577db9bacc51f8e9516419ae8f66">  198</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#a0203577db9bacc51f8e9516419ae8f66" title="Set the kernel width (equivalent to setting the bandwidth).">setWidth</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#acddb38cbf7f7eb1ddb915020cacd9416" title="Kernel width parameter, equivalent to the bandwidth parameter.">sigma</a>)</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>    {</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(<a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#acddb38cbf7f7eb1ddb915020cacd9416" title="Kernel width parameter, equivalent to the bandwidth parameter.">sigma</a> &gt; 0.0);</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>        <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a> = 1.0 / (2.0 * <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#acddb38cbf7f7eb1ddb915020cacd9416" title="Kernel width parameter, equivalent to the bandwidth parameter.">sigma</a> * <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#acddb38cbf7f7eb1ddb915020cacd9416" title="Kernel width parameter, equivalent to the bandwidth parameter.">sigma</a>);</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>    }</div>
</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span><span class="comment"></span> </div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span><span class="comment">    /// From ISerializable.</span></div>
<div class="foldopen" id="foldopen00205" data-start="{" data-end="}">
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#ac89541fe765cdc20608138848b66ac59">  205</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#ac89541fe765cdc20608138848b66ac59" title="From ISerializable.">read</a>(<a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a>&amp; ar)</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    {</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        <a class="code hl_function" href="classshark_1_1_discrete_kernel.html#af1eb1494ee1d205dab4f4b276f332de6" title="From ISerializable.">base_type::read</a>(ar);</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        ar &gt;&gt; <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a>;</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>    }</div>
</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="comment"></span> </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span><span class="comment">    /// From ISerializable.</span></div>
<div class="foldopen" id="foldopen00212" data-start="{" data-end="}">
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#aa82b43af690db009d1a2c0f052451f29">  212</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#aa82b43af690db009d1a2c0f052451f29" title="From ISerializable.">write</a>(<a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a>&amp; ar)<span class="keyword"> const</span></div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <a class="code hl_function" href="classshark_1_1_discrete_kernel.html#aa6ceb9cefe199070f62832348f9c4421" title="From ISerializable.">base_type::write</a>(ar);</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>        ar &lt;&lt; <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a>;</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>    }</div>
</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span> </div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment"></span> </div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span><span class="comment">    /// \brief Compute the Gram matrix of the task kernel.</span></div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span><span class="comment">    /// \par</span></div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span><span class="comment">    /// Here is the real meat. This function implements the</span></div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span><span class="comment">    /// kernel function defined in&lt;br/&gt;</span></div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span><span class="comment">    /// Learning Marginal Predictors: Transfer to an Unlabeled Task.</span></div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span><span class="comment">    /// G. Blanchard, G. Lee, C. Scott.</span></div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span><span class="comment">    /// \par</span></div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span><span class="comment">    /// In a first step the function computes the inner products</span></div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span><span class="comment">    /// of the task-wise empirical distributions, represented by</span></div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span><span class="comment">    /// their mean elements in the kernel-induced feature space.</span></div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span><span class="comment">    /// In a second step this information is used for the computation</span></div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span><span class="comment">    /// of squared distances between empirical distribution, which</span></div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span><span class="comment">    /// allows for the straightforward computation of a Gaussian</span></div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span><span class="comment">    /// kernel.</span></div>
<div class="foldopen" id="foldopen00236" data-start="{" data-end="}">
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#aefbd61ad0c2f8c4adb06669bd49a41f1">  236</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#aefbd61ad0c2f8c4adb06669bd49a41f1" title="Compute the Gram matrix of the task kernel.">computeMatrix</a>()</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>    {</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>        <span class="comment">// count number of examples for each task</span></div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>        <span class="keyword">const</span> std::size_t tasks = <a class="code hl_function" href="classshark_1_1_gaussian_task_kernel.html#acd73ed4a70e91cc3d8f85c70fac37247">numberOfTasks</a>();</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>        std::size_t elements = <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.numberOfElements();</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        std::vector&lt;std::size_t&gt; ell(tasks, 0);</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;elements; i++)</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>            ell[<a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.element(i).task]++;</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span> </div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>        <span class="comment">// compute inner products between mean elements of empirical distributions</span></div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;elements; i++){</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>            <span class="keyword">const</span> std::size_t task_i = <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.element(i).task;</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            <span class="keywordflow">for</span> (std::size_t j=0; j&lt;i; j++){</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>                <span class="keyword">const</span> std::size_t task_j = <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.element(j).task;</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>                <span class="keyword">const</span> <span class="keywordtype">double</span> k = <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(<a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.element(i).input, <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.element(j).input);</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>                <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(task_i, task_j) += k;</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>                <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(task_j, task_i) += k;</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>            }</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>            <span class="keyword">const</span> <span class="keywordtype">double</span> k = <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>-&gt;<a class="code hl_function" href="classshark_1_1_abstract_kernel_function.html#abd10e3815efade90c7f9e2a7cc8bcb6c" title="Evaluates the kernel function.">eval</a>(<a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.element(i).input, <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>.element(i).input);</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>            <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(task_i, task_i) += k;</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        }</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;tasks; i++){</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>            <span class="keywordflow">if</span> (ell[i] == 0) <span class="keywordflow">continue</span>;</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>            <span class="keywordflow">for</span> (std::size_t j=0; j&lt;tasks; j++){</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>                <span class="keywordflow">if</span> (ell[j] == 0) <span class="keywordflow">continue</span>;</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>                <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(i, j) /= (double)(ell[i] * ell[j]);</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>            }</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>        }</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span> </div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        <span class="comment">// compute Gaussian kernel</span></div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;tasks; i++)</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>        {</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>            <span class="keyword">const</span> <span class="keywordtype">double</span> norm2_i = <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(i, i);</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>            <span class="keywordflow">for</span> (std::size_t j=0; j&lt;i; j++)</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>            {</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>                <span class="keyword">const</span> <span class="keywordtype">double</span> norm2_j = <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(j, j);</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>                <span class="keyword">const</span> <span class="keywordtype">double</span> dist2 = norm2_i + norm2_j - 2.0 * <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(i, j);</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>                <span class="keyword">const</span> <span class="keywordtype">double</span> k = std::exp(-<a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a> * dist2);</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>                <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(i, j) = <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(j, i) = k;</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span>            }</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>        }</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;tasks; i++) <a class="code hl_variable" href="classshark_1_1_discrete_kernel.html#a587d3fa95d39fcb80e942e3d201755ad" title="kernel matrix">base_type::m_matrix</a>(i, i) = 1.0;</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>    }</div>
</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span> </div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span> </div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909">  281</a></span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;MultiTaskSampleType &gt;</a> <span class="keyword">const</span>&amp; <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5e8007b13b2c708f693b06cd48872909" title="multi-task data">m_data</a>;  <span class="comment">///&lt; multi-task data</span></div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a">  282</a></span>    <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a56ca3c04e44896711e14dbea30325c6a" title="kernel on inputs">mpe_inputKernel</a>;            <span class="comment">///&lt; kernel on inputs</span></div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67">  283</a></span>    <span class="keywordtype">double</span> <a class="code hl_variable" href="classshark_1_1_gaussian_task_kernel.html#a5de378357c9dc154b1eca425849c1f67" title="bandwidth of the Gaussian task kernel">m_gamma</a>;                        <span class="comment">///&lt; bandwidth of the Gaussian task kernel</span></div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>};</div>
</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span> </div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span><span class="comment"></span> </div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span><span class="comment">///</span></div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span><span class="comment">/// \brief Special kernel function for multi-task and transfer learning.</span></div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span><span class="comment">///</span></div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span><span class="comment">/// \par</span></div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span><span class="comment">/// This class is a convenience wrapper for the product of an</span></div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span><span class="comment">/// input kernel and a kernel on tasks. It also encapsulates</span></div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span><span class="comment">/// the projection from multi-task learning data (see class</span></div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span><span class="comment">/// MultiTaskSample) to inputs and task indices.</span></div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span><span class="comment">///</span></div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputTypeT&gt;</div>
<div class="foldopen" id="foldopen00297" data-start="{" data-end="};">
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_task_kernel.html">  297</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_multi_task_kernel.html" title="Special kernel function for multi-task and transfer learning.">MultiTaskKernel</a></div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>: <span class="keyword">private</span> detail::MklKernelBase&lt;MultiTaskSample&lt;InputTypeT&gt; &gt;</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_product_kernel.html" title="Product of kernel functions.">ProductKernel</a>&lt; MultiTaskSample&lt;InputTypeT&gt; &gt;</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>{</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>    <span class="keyword">typedef</span> detail::MklKernelBase&lt;MultiTaskSample&lt;InputTypeT&gt; &gt; base_type1;</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_product_kernel.html" title="Product of kernel functions.">ProductKernel&lt; MultiTaskSample&lt;InputTypeT&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_product_kernel.html">base_type2</a>;</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_task_kernel.html#a0d051d2915088b7675e02fd1af7088e4">  305</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputTypeT&gt;</a> <a class="code hl_typedef" href="classshark_1_1_multi_task_kernel.html#a0d051d2915088b7675e02fd1af7088e4">InputKernelType</a>;<span class="comment"></span></div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span><span class="comment">    /// \brief Constructor.</span></div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span><span class="comment">    /// \param  inputkernel  kernel on inputs</span></div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span><span class="comment">    /// \param  taskkernel   kernel on task indices</span></div>
<div class="foldopen" id="foldopen00310" data-start="{" data-end="}">
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_task_kernel.html#a51d1610417a9bb6cc4e71725feb39cac">  310</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_multi_task_kernel.html#a51d1610417a9bb6cc4e71725feb39cac" title="Constructor.">MultiTaskKernel</a>(</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>        <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">InputKernelType</a>* inputkernel,</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>        <a class="code hl_class" href="classshark_1_1_discrete_kernel.html" title="Kernel on a finite, discrete space.">DiscreteKernel</a>* taskkernel)</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>    :base_type1(<a class="code hl_namespace" href="namespaceboost.html">boost</a>::fusion::make_vector(inputkernel,taskkernel))</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>    ,<a class="code hl_class" href="classshark_1_1_product_kernel.html">base_type2</a>(base_type1::makeKernelVector())</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>    {}</div>
</div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span><span class="comment"></span> </div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00318" data-start="{" data-end="}">
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno"><a class="line" href="classshark_1_1_multi_task_kernel.html#a4f294206774e0579a2ba64d445660ffb">  318</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_multi_task_kernel.html#a4f294206774e0579a2ba64d445660ffb" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;MultiTaskKernel&quot;</span>; }</div>
</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>};</div>
</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span> </div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>} <span class="comment">// namespace shark {</span></div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span> </div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span><span class="preprocessor">#endif</span></div>
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